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Really cool write-up, so I figure it's worth airing my ignorance. It's not enough to know that the system correctly categorized the two test images. It could j
by akkartik 9y ago
Really cool write-up, so I figure it's worth airing my ignorance.
It's not enough to know that the system correctly categorized the two test images. It could just be luck, since it's such a tiny input set.
One way to gain confidence that the correct rule was learned would be to use the correct answer to generate another dozen (or hundred) test images for both sides. Then the question would be whether all the test images are correctly categorized based on training over the initial dozen (or ten) images.
Does this suggestion sound reasonable to someone with ML experience?
- coolvision 9y agoYes, I'm thinking about exactly the same way to prove understanding of the rule, it's in the end of the post: >>> Generative NN architectures could be used as well, like variational autoencoder (VAE), or generative adversarial networks (GAN). In this case, it would be “explanation by example”. [...] NN would generate new examples of images from Bongard problems, and if this generated images capture the concepts behind classification rule, it might be enough to show understanding of the problem.